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Data Engineering →mediumMultiple ChoiceObjective-mapped

MLS-C01 Data Engineering Practice Question

This MLS-C01 practice question tests your understanding of data engineering. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A data engineering team uses Amazon EMR with Spark to transform large datasets in S3. The team notices that the Spark jobs on the EMR cluster are failing with out-of-memory errors. The cluster uses instance types with moderate memory. Which configuration change would MOST effectively reduce memory pressure without increasing cost?

A
Use a larger EMR cluster with more instances of the same type.
Why wrong: Adding instances increases cost.
B
Increase the number of executor cores to improve parallelism.
Why wrong: More cores can increase concurrency but may increase memory usage per executor.
C
Switch from Java serialization to Kryo serialization.
Why wrong: Kryo reduces object serialization size but does not directly reduce shuffle data volume as much as compression.
D
Enable shuffle compression and set spark.shuffle.compress to true.
Compression reduces the amount of data stored in memory during shuffles.

Full breakdown with real-world context →

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Enable shuffle compression and set spark.shuffle.compress to true.

Option D is correct because enabling shuffle compression (spark.shuffle.compress=true) reduces the amount of data written to disk and stored in memory during shuffle operations, thereby lowering memory pressure without changing the cluster size or cost. Option A is incorrect because adding more instances increases cost and does not directly reduce memory per task. Option B is incorrect because increasing executor cores may increase parallelism but can also increase memory usage per task if not properly configured, and does not directly reduce memory pressure. Option C is incorrect because while Kryo serialization reduces object serialization overhead, it does not significantly reduce shuffle data size; shuffle compression is more effective for reducing memory usage during shuffle.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Use a larger EMR cluster with more instances of the same type.
Why it's wrong here
Adding instances increases cost.
✗
Increase the number of executor cores to improve parallelism.
Why it's wrong here
More cores can increase concurrency but may increase memory usage per executor.
✗
Switch from Java serialization to Kryo serialization.
Why it's wrong here
Kryo reduces object serialization size but does not directly reduce shuffle data volume as much as compression.
✓
Enable shuffle compression and set spark.shuffle.compress to true.
Why this is correct
Compression reduces the amount of data stored in memory during shuffles.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

What to study next

Got this wrong? Here's your next step.

Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Enable shuffle compression and set spark.shuffle.compress to true. — Option D is correct because enabling shuffle compression (spark.shuffle.compress=true) reduces the amount of data written to disk and stored in memory during shuffle operations, thereby lowering memory pressure without changing the cluster size or cost. Option A is incorrect because adding more instances increases cost and does not directly reduce memory per task. Option B is incorrect because increasing executor cores may increase parallelism but can also increase memory usage per task if not properly configured, and does not directly reduce memory pressure. Option C is incorrect because while Kryo serialization reduces object serialization overhead, it does not significantly reduce shuffle data size; shuffle compression is more effective for reducing memory usage during shuffle.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.